# Copyright (c) 2021-2022, NVIDIA Corporation & Affiliates. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://github.com/NVlabs/FAN/blob/main/LICENSE

""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
    - https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
#
# Modified by: Daquan Zhou
# --------------------------------------------------------

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_

# from mmcv_custom import load_checkpoint
from mmcv.runner import load_checkpoint
from mmseg.utils import get_root_logger
from ..builder import BACKBONES

import math


class Mlp(nn.Module):
    """ Multilayer perceptron."""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x
class PositionalEncodingFourier(nn.Module):
    """
    Positional encoding relying on a fourier kernel matching the one used in the "Attention is all of Need" paper.
    Based on the official XCiT code
        - https://github.com/facebookresearch/xcit/blob/master/xcit.py
    """

    def __init__(self, hidden_dim=32, dim=768, temperature=10000):
        super().__init__()
        self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
        self.scale = 2 * math.pi
        self.temperature = temperature
        self.hidden_dim = hidden_dim
        self.dim = dim
        self.eps = 1e-6

    def forward(self, B: int, H: int, W: int):
        device = self.token_projection.weight.device
        y_embed = torch.arange(1, H+1, dtype=torch.float32, device=device).unsqueeze(1).repeat(1, 1, W)
        x_embed = torch.arange(1, W+1, dtype=torch.float32, device=device).repeat(1, H, 1)
        y_embed = y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
        x_embed = x_embed / (x_embed[:, :, -1:] + self.eps) * self.scale
        dim_t = torch.arange(self.hidden_dim, dtype=torch.float32, device=device)
        dim_t = self.temperature ** (2 * torch.floor(torch.div(dim_t, 2)) / self.hidden_dim)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack([pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()], dim=4).flatten(3)
        pos_y = torch.stack([pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()], dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        pos = self.token_projection(pos)
        return pos.repeat(B, 1, 1, 1)  # (B, C, H, W)
class FANMlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        # self.dwconv = DWConv(hidden_features)
        self.dwconv = LPI(hidden_features)
        self.gamma = nn.Parameter(torch.ones(hidden_features), requires_grad=True)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
        self.linear = linear
        if self.linear:
            self.relu = nn.ReLU(inplace=True)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        x = self.fc1(x)
        if self.linear:
            x = self.relu(x)
        # import pdb; pdb.set_trace()
        # add in conv in the block forward
        x = self.drop(self.gamma * self.dwconv(x, H, W)) + x
        # x = self.dwconv(x, H, W)
        # x = self.act(x)
        # x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
class LPI(nn.Module):
    """
    Local Patch Interaction module that allows explicit communication between tokens in 3x3 windows to augment the
    implicit communication performed by the block diagonal scatter attention. Implemented using 2 layers of separable
    3x3 convolutions with GeLU and BatchNorm2d
    """

    def __init__(self, in_features, out_features=None, act_layer=nn.GELU, kernel_size=3):
        super().__init__()
        out_features = out_features or in_features

        padding = kernel_size // 2

        self.conv1 = torch.nn.Conv2d(
            in_features, in_features, kernel_size=kernel_size, padding=padding, groups=in_features)
        self.act = act_layer()
        self.bn = nn.BatchNorm2d(in_features)
        self.conv2 = torch.nn.Conv2d(
            in_features, out_features, kernel_size=kernel_size, padding=padding, groups=out_features)

    def forward(self, x, H: int, W: int):
        B, N, C = x.shape
        x = x.permute(0, 2, 1).reshape(B, C, H, W)
        x = self.conv1(x)
        x = self.act(x)
        x = self.bn(x)
        x = self.conv2(x)
        x = x.reshape(B, C, N).permute(0, 2, 1)
        return x
class ChannelSA_tmp3(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., 
                    sr_ratio=1, linear=False, share_atten=False, drop_path=0., emlp=True, spatial_attn=False,
                    mlp_hidden_dim=None, act_layer=nn.GELU, drop=0., norm_layer=nn.LayerNorm, sampling_ratio=1, cha_sr_ratio=1, c_head_num=8):
        """
            Three main modifications:
                1. use random sampling to reduce spatial resolution.
                2. add in channel dimension reduction.
                3. move conv block into V matrix processing
        """
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim

        num_heads = c_head_num or num_heads
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # self.scale = qk_scale or head_dim ** -0.5
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.cha_sr_ratio = cha_sr_ratio if num_heads > 1 else 1

        self.share_atten = share_atten
        self.emlp = emlp

        self.idx_q = self.idx_k = None
        self.offset = None
        self.sampling_ratio = sampling_ratio

        self.spatial_attn = spatial_attn

        
        # self.gamma = nn.Parameter(1. * torch.ones(dim), requires_grad=True)

        # config of mlp for v processing
        # self.norm_attn = norm_layer(dim)
        if emlp:
            self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
            self.mlp_v = FANMlp(in_features=dim//self.cha_sr_ratio, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
            self.norm_v = norm_layer(dim//self.cha_sr_ratio)

        if share_atten:
            self.adapt_conv = InvertedResidual(self.num_heads,self.num_heads, expand_ratio=3, kernel_size=3)
            # self.adapt_conv = Conv2dSamePadding(self.num_heads,self.num_heads, 3, 1)
            self.adapt_bn = nn.BatchNorm2d(self.num_heads)

            self.kv = nn.Linear(dim, dim * 1, bias=qkv_bias)
        else:
            self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.emlp = emlp
        if not emlp:
            self.proj = nn.Linear(dim, dim)
            self.proj_drop = nn.Dropout(proj_drop)

        self.linear = linear
        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()
    def _gen_attn(self, q, k, mode='none', shift_range=4, sampling_step = 4):
        # q = torch.nn.functional.normalize(q.transpose(-1, -2), dim=-1)
        # k = torch.nn.functional.normalize(k.transpose(-1, -2), dim=-1)
        q = q.softmax(-2).transpose(-1,-2)
        B, H, N, C  = k.shape
        k = torch.nn.functional.adaptive_avg_pool2d(k.softmax(-2), (N, 1))
        # import pdb; pdb.set_trace()
        # k = k.softmax(-2).mean(-1).unsqueeze(-1)
        
        if 'sampling' in mode and sampling_step > 1:
            shift_range = sampling_step
            if self.idx_q is None:
                shape = q.shape
                idx = torch.LongTensor([[[[i for i in range(0, shape[-1], sampling_step)]] * shape[2]] * shape[1]]*shape[0])
                if mode == 'uniform_sampling':
                    self.offset = nn.Parameter(F.normalize(torch.randn(idx.shape), dim=-1) * shift_range, requires_grad=False)
                    idx = torch.clamp(idx + self.offset, min=0, max=shape[-1]-1).long().cuda()
                    self.idx = idx if q.get_device() == -1 else idx.cuda()
                    self.idx_q = nn.Parameter(idx.clone(), requires_grad=False).cuda()
                    self.idx_k = nn.Parameter(idx[:, :, : shape[2] // self.cha_sr_ratio, :].clone(), requires_grad=False).cuda()
                else:
                    self.offset = F.normalize(torch.randn(idx.shape), dim=-1) * shift_range
                    idx = torch.clamp(idx + self.offset, min=0, max=shape[-1]-1).long().cuda()
                    self.idx = idx if q.get_device() == -1 else idx.cuda()
                    self.idx_q = idx.clone()
                    self.idx_k = idx[:, :, : shape[2] // self.cha_sr_ratio, :].clone()

            if self.idx_q.shape == q.shape:
                attn = q.gather(-1, self.idx_q) @ k.gather(-1, self.idx_k).transpose(-2, -1)
            else:
                shape = q.shape
                attn = q.gather(-1, self.idx_q[:shape[0], :, :, :]) @ k.gather(-1, self.idx_k[:shape[0]]).transpose(-2, -1)
        else:
            attn = torch.nn.functional.sigmoid(q @ k)
        return attn  * self.temperature# ).softmax(-1)
        # return (attn * self.temperature).softmax(-1)
    def forward(self, x, H, W, atten=None):
        B, N, C = x.shape
        if not self.share_atten:
            if self.spatial_attn:
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
            else:
                # import pdb; pdb.set_trace()
                # v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads // self.cha_sr_ratio).permute(0, 2, 1, 3)
                v = x.reshape(B, N, self.num_heads, C // self.num_heads // self.cha_sr_ratio).permute(0, 2, 1, 3)

        # import pdb;pdb.set_trace()
        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            if self.share_atten:
                kv = self.kv(x_).reshape(B, -1, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
            else:
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
                k = self.k(x).reshape(B, N, self.num_heads,  C // self.num_heads).permute(0, 2, 1, 3)
        else:
            if self.share_atten:
                kv = self.kv(x).reshape(B, -1, 1, self.num_heads, C // self.num_heads // self.cha_sr_ratio).permute(2, 0, 3, 1, 4)
            else:
                
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
                # k = self.k(x).reshape(B, N, self.num_heads,  self.num_heads // self.num_heads // self.cha_sr_ratio).permute(0, 2, 1, 3)
                # q = x.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
                k = x.reshape(B, N, self.num_heads,  C // self.num_heads).permute(0, 2, 1, 3)

        if self.share_atten:
            v = kv[0]
            attn = self.adapt_bn(self.adapt_conv(atten))
        else:
            if self.spatial_attn:
                k, v = kv[0], kv[1]
                q = torch.nn.functional.normalize(q.transpose(-1, -2), dim=-1)
                k = torch.nn.functional.normalize(k.transpose(-1, -2), dim=-1)
                attn = (q @ k.transpose(-2, -1)) * self.scale
                attn = attn.softmax(dim=-1)
            else:
                attn = self._gen_attn(q, k, sampling_step=self.sampling_ratio)
        attn = self.attn_drop(attn)

        # move mlp here to process v
        if self.emlp:
            Bv, Hd, Nv, Cv = v.shape
            # import pdb;pdb.set_trace()
            v = self.norm_v(self.mlp_v(v.transpose(1, 2).reshape(Bv, Nv, Hd*Cv), H//self.sr_ratio, W//self.sr_ratio)).reshape(Bv, Nv, Hd, Cv).transpose(1, 2).contiguous()
            # v = v + self.drop_path(self.mlp_v(self.norm_v(v.transpose(1, 2).reshape(Bv, Nv, Hd*Cv)), H//self.sr_ratio, W//self.sr_ratio)).reshape(Bv, Nv, Hd, Cv).transpose(1, 2)
        if self.spatial_attn:
            x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        else:
            # x = (attn @ v).transpose(1, 2).reshape(B, N, C)
            # import pdb; pdb.set_trace()
            repeat_time = N // attn.shape[-1]
            attn = attn.repeat_interleave(repeat_time, dim=-1) if attn.shape[-1] > 1 else attn
            x = (attn * v.transpose(-1, -2)).permute(0, 3, 1, 2).reshape(B, N, C)
        if not self.emlp:
            x = self.proj(x)
            x = self.proj_drop(x)
#         import pdb;pdb.set_trace()
        return x#,  attn * v.transpose(-1, -2) #attn
    @torch.jit.ignore
    def no_weight_decay(self):
        return {'temperature'}
class ChannelSA(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., 
                    sr_ratio=1, linear=False, share_atten=False, drop_path=0., emlp=True, spatial_attn=False,
                    mlp_hidden_dim=None, act_layer=nn.GELU, drop=0., norm_layer=nn.LayerNorm, sampling_ratio=1, cha_sr_ratio=1):
        """
            Three main modifications:
                1. use random sampling to reduce spatial resolution.
                2. add in channel dimension reduction.
                3. move conv block into V matrix processing
        """
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # self.scale = qk_scale or head_dim ** -0.5
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.cha_sr_ratio = cha_sr_ratio if num_heads > 1 else 1

        self.share_atten = share_atten
        self.emlp = emlp

        self.idx_q = self.idx_k = None
        self.offset = None
        self.sampling_ratio = sampling_ratio

        self.spatial_attn = spatial_attn

        
        # self.gamma = nn.Parameter(1. * torch.ones(dim), requires_grad=True)

        # config of mlp for v processing
        if emlp:
            self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
            self.mlp_v = FANMlp(in_features=dim//self.cha_sr_ratio, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
            self.norm_v = norm_layer(dim//self.cha_sr_ratio)

        if share_atten:
            self.adapt_conv = InvertedResidual(self.num_heads,self.num_heads, expand_ratio=3, kernel_size=3)
            # self.adapt_conv = Conv2dSamePadding(self.num_heads,self.num_heads, 3, 1)
            self.adapt_bn = nn.BatchNorm2d(self.num_heads)

            self.kv = nn.Linear(dim, dim * 1, bias=qkv_bias)
        else:
            self.q = nn.Linear(dim, dim, bias=qkv_bias)
            self.k = nn.Linear(dim, dim//self.cha_sr_ratio, bias=qkv_bias)
            self.v = nn.Linear(dim, dim//self.cha_sr_ratio, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.emlp = emlp
        if not emlp:
            self.proj = nn.Linear(dim, dim)
            self.proj_drop = nn.Dropout(proj_drop)

        self.linear = linear
        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()
    def _gen_attn(self, q, k, mode='none', shift_range=4, sampling_step = 4):
        q = torch.nn.functional.normalize(q.transpose(-1, -2), dim=-1)
        k = torch.nn.functional.normalize(k.transpose(-1, -2), dim=-1)
        
        if 'sampling' in mode and sampling_step > 1:
            shift_range = sampling_step
            if self.idx_q is None:
                shape = q.shape
                idx = torch.LongTensor([[[[i for i in range(0, shape[-1], sampling_step)]] * shape[2]] * shape[1]]*shape[0])
                if mode == 'uniform_sampling':
                    self.offset = nn.Parameter(F.normalize(torch.randn(idx.shape), dim=-1) * shift_range, requires_grad=False)
                    idx = torch.clamp(idx + self.offset, min=0, max=shape[-1]-1).long().cuda()
                    self.idx = idx if q.get_device() == -1 else idx.cuda()
                    self.idx_q = nn.Parameter(idx.clone(), requires_grad=False).cuda()
                    self.idx_k = nn.Parameter(idx[:, :, : shape[2] // self.cha_sr_ratio, :].clone(), requires_grad=False).cuda()
                else:
                    self.offset = F.normalize(torch.randn(idx.shape), dim=-1) * shift_range
                    idx = torch.clamp(idx + self.offset, min=0, max=shape[-1]-1).long().cuda()
                    self.idx = idx if q.get_device() == -1 else idx.cuda()
                    self.idx_q = idx.clone()
                    self.idx_k = idx[:, :, : shape[2] // self.cha_sr_ratio, :].clone()

            if self.idx_q.shape == q.shape:
                attn = q.gather(-1, self.idx_q) @ k.gather(-1, self.idx_k).transpose(-2, -1)
            else:
                shape = q.shape
                attn = q.gather(-1, self.idx_q[:shape[0], :, :, :]) @ k.gather(-1, self.idx_k[:shape[0]]).transpose(-2, -1)
        else:
            # import pdb; pdb.set_trace()
            attn = q @ k.transpose(-2, -1)
        return (attn * self.temperature).softmax(-1)
    def forward(self, x, H, W, atten=None):
        B, N, C = x.shape
        if not self.share_atten:
            if self.spatial_attn:
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
            else:
                # import pdb; pdb.set_trace()
                v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads // self.cha_sr_ratio).permute(0, 2, 1, 3)

        # import pdb;pdb.set_trace()
        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            if self.share_atten:
                kv = self.kv(x_).reshape(B, -1, 1, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
            else:
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
                k = self.k(x).reshape(B, N, self.num_heads,  C // self.num_heads).permute(0, 2, 1, 3)
        else:
            if self.share_atten:
                kv = self.kv(x).reshape(B, -1, 1, self.num_heads, C // self.num_heads // self.cha_sr_ratio).permute(2, 0, 3, 1, 4)
            else:
                
                q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
                k = self.k(x).reshape(B, N, self.num_heads,  C // self.num_heads // self.cha_sr_ratio).permute(0, 2, 1, 3)

        if self.share_atten:
            v = kv[0]
            attn = self.adapt_bn(self.adapt_conv(atten))
        else:
            if self.spatial_attn:
                k, v = kv[0], kv[1]
                q = torch.nn.functional.normalize(q.transpose(-1, -2), dim=-1)
                k = torch.nn.functional.normalize(k.transpose(-1, -2), dim=-1)
                attn = (q @ k.transpose(-2, -1)) * self.scale
                attn = attn.softmax(dim=-1)
            else:
                attn = self._gen_attn(q, k, sampling_step=self.sampling_ratio)
        attn = self.attn_drop(attn)

        # move mlp here to process v
        if self.emlp:
            Bv, Hd, Nv, Cv = v.shape
            # import pdb;pdb.set_trace()
            v = self.norm_v(self.mlp_v(v.transpose(1, 2).reshape(Bv, Nv, Hd*Cv), H//self.sr_ratio, W//self.sr_ratio)).reshape(Bv, Nv, Hd, Cv).transpose(1, 2)
            # v = v + self.drop_path(self.mlp_v(self.norm_v(v.transpose(1, 2).reshape(Bv, Nv, Hd*Cv)), H//self.sr_ratio, W//self.sr_ratio)).reshape(Bv, Nv, Hd, Cv).transpose(1, 2)
            # 
        if self.spatial_attn:
            x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        else:
            # x = (attn @ v).transpose(1, 2).reshape(B, N, C)
            x = (attn @ v.transpose(-1, -2)).permute(0, 3, 1, 2).reshape(B, N, C)
        if not self.emlp:
            x = self.proj(x)
            x = self.proj_drop(x)
#         import pdb;pdb.set_trace()
        return x#,  attn @ v.transpose(-1, -2) #attn
    @torch.jit.ignore
    def no_weight_decay(self):
        return {'temperature'}
class WindowAttention(nn.Module):
    """ Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """ Forward function.
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)

        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class SwinTransformerBlock(nn.Module):
    """ Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, num_heads, window_size=7, shift_size=0, mlp_type=None,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp_type = mlp_type
        if mlp_type == 'Mlp':
            self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        else:
            self.mlp = ChannelSA_tmp3(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, 
                                        drop_path=drop_path, mlp_hidden_dim = mlp_hidden_dim, emlp=True)
        self.H = None
        self.W = None

    def forward(self, x, mask_matrix):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
            mask_matrix: Attention mask for cyclic shift.
        """
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # pad feature maps to multiples of window size
        pad_l = pad_t = 0
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x

        if pad_r > 0 or pad_b > 0:
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        if self.mlp_type == 'Mlp':
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        return x


class PatchMerging(nn.Module):
    """ Patch Merging Layer
    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """
    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x, H, W):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"

        x = x.view(B, H, W, C)

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x


class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (int): Local window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self,
                 dim,
                 depth,
                 num_heads,
                 window_size=7,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 qk_scale=None,
                 drop=0.,
                 attn_drop=0.,
                 drop_path=0.,
                 mlp_type='Mlp',
                 norm_layer=nn.LayerNorm,
                 downsample=None,
                 use_checkpoint=False):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(
                dim=dim,
                num_heads=num_heads,
                window_size=window_size,
                mlp_type=mlp_type,
                shift_size=0 if (i % 2 == 0) else window_size // 2,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop,
                attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def forward(self, x, H, W):
        """ Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """

        # calculate attention mask for SW-MSA
        Hp = int(np.ceil(H / self.window_size)) * self.window_size
        Wp = int(np.ceil(W / self.window_size)) * self.window_size
        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
        h_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        w_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

        for blk in self.blocks:
            blk.H, blk.W = H, W
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask)
            else:
                x = blk(x, attn_mask)
        if self.downsample is not None:
            x_down = self.downsample(x, H, W)
            Wh, Ww = (H + 1) // 2, (W + 1) // 2
            return x, H, W, x_down, Wh, Ww
        else:
            return x, H, W, x, H, W


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    Args:
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        """Forward function."""
        # padding
        _, _, H, W = x.size()
        if W % self.patch_size[1] != 0:
            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
        if H % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))

        x = self.proj(x)  # B C Wh Ww
        B, C, H, W = x.shape
        if self.norm is not None:
            Wh, Ww = x.size(2), x.size(3)
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)

        return x, (H, W)


@BACKBONES.register_module()
class FANSwinTransformer(nn.Module):
    """ Swin Transformer backbone.
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030
    Args:
        pretrain_img_size (int): Input image size for training the pretrained model,
            used in absolute postion embedding. Default 224.
        patch_size (int | tuple(int)): Patch size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        depths (tuple[int]): Depths of each Swin Transformer stage.
        num_heads (tuple[int]): Number of attention head of each stage.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): Dropout rate.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
        out_indices (Sequence[int]): Output from which stages.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(self,
                 pretrain_img_size=224,
                 patch_size=4,
                 in_chans=3,
                 embed_dim=96,
                 style=None,
                 depths=[2, 2, 18, 2],
                 num_heads=[3, 6, 12, 24],
                 window_size=7,
                 mlp_ratio=4.,
                 mlp_type = ['FAN', 'FAN', 'FAN', 'Mlp'],
                 qkv_bias=True,
                 qk_scale=None,
                 drop_rate=0.,
                 attn_drop_rate=0.,
                 drop_path_rate=0.2,
                 norm_layer=nn.LayerNorm,
                 ape=False,
                 patch_norm=True,
                 out_indices=(0, 1, 2, 3),
                 frozen_stages=-1,
                 use_checkpoint=False):
        super().__init__()

        self.pretrain_img_size = pretrain_img_size
        # import pdb; pdb.set_trace()
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.out_indices = out_indices
        self.frozen_stages = frozen_stages

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)

        # absolute position embedding
        if self.ape:
            pretrain_img_size = to_2tuple(pretrain_img_size)
            patch_size = to_2tuple(patch_size)
            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]

            # self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
            self.absolute_pos_embed = PositionalEncodingFourier(dim=embed_dim)
            # trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                depth=depths[i_layer],
                mlp_type = mlp_type[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint)
            self.layers.append(layer)

        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
        self.num_features = num_features

        # add a norm layer for each output
        for i_layer in out_indices:
            layer = norm_layer(num_features[i_layer])
            layer_name = f'norm{i_layer}'
            self.add_module(layer_name, layer)

        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False

        if self.frozen_stages >= 1 and self.ape:
            self.absolute_pos_embed.requires_grad = False

        if self.frozen_stages >= 2:
            self.pos_drop.eval()
            for i in range(0, self.frozen_stages - 1):
                m = self.layers[i]
                m.eval()
                for param in m.parameters():
                    param.requires_grad = False

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.
        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        if isinstance(pretrained, str):
            self.apply(_init_weights)
            logger = get_root_logger()
            load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
        elif pretrained is None:
            self.apply(_init_weights)
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x):
        """Forward function."""
        x , (H,W)= self.patch_embed(x)

        B, Wh, Ww = x.size(0), x.size(2), x.size(3)
        if self.ape:
            # interpolate the position embedding to the corresponding size
            # absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
            # x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
            x = (x + self.absolute_pos_embed(B, H, W)).reshape(B, -1, x.shape[1])# .permute(0, 2, 1)
            # import pdb; pdb.set_trace()
        else:
            x = x.flatten(2).transpose(1, 2)
        x = self.pos_drop(x)

        outs = []
        for i in range(self.num_layers):
            layer = self.layers[i]
            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)

            if i in self.out_indices:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x_out)

                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        return tuple(outs)

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(FANSwinTransformer, self).train(mode)
        self._freeze_stages()
